This week, I share hard-won lessons from building a GraphRAG application with Neo4j in Python, plus standout tips from Lize Raes's Devoxx Belgium talk on taking Langchain4j apps to production.
GraphRAG with Neo4j
- Built a Python GraphRAG app using the Neo4j GraphRAG package — knowledge graph construction, retrievers (vector, graph, text-to-cypher), and agentic orchestration
- Key lesson: don't let the LLM decide your entire data model. Providing node types, relationship types, and patterns as boundaries dramatically improves results
- Expect iteration — retrieval testing will send you back to refine your KG construction
- Github code: Neo4j GraphRAG Python package
Langchain4j for Production (Lize Raes, Devoxx Belgium)
- Wrap RAG as an agent tool for multi-call retrieval instead of single-shot pipelines
- Filter available tools programmatically by domain to keep agents focused
- Wire sub-agents as @Tool for clean multi-agent orchestration
- Use immediate responses to skip the LLM summarization hop — saves tokens and latency
- 13-step walkthrough for production-grade agentic systems
- YouTube link: Level Up Your Langchain4j Apps for Production (Lize Raes, Devoxx Belgium 2025)
